1. Field of Invention
Embodiments of the invention generally relate to object recognition. More specifically, at least one embodiment relates to object recognition using morphologically-processed images.
2. Discussion of Related Art
Object recognition is employed in a wide variety of fields to locate real world objects that appear in images recorded by cameras or other image acquisition devices. For example, object recognition is used in manufacturing, medical imaging, security and transportation. Computer vision systems and/or machine vision systems often operate to acquire, process and analyze images to extract information in the preceding and other applications.
However, the appearance of an object in the acquired image can widely vary due to variations in the object itself (e.g., due to variations in the manufacturing process, degradation over time, etc.), lighting conditions during image acquisition, the object's orientation, the object's distance from the image acquisition system, the object's motion, occlusion of the object, characteristics of the image acquisition device and other factors. As a result, the problem of object recognition is not always readily solved.
Model-based approaches provide one current form of object recognition. According to these approaches, a user provides a system with an image of an object for which occurrences are to be recognized. Such an image can be an image acquired by a camera or a synthetic image output by a software application. This image serves as a “model” image that can be used by the system to determine the occurrence of the object in images that are subsequently analyzed by the system. For example, a “target” image can be analyzed to locate an occurrence of the object represented in the model image. In general, the object recognition is performed by analyzing the target image compared to the model image. According to some approaches, values of pixels in the target image are compared with the values of pixels in the model image. Other approaches extract features from the target image (for example, the edges of objects within the target image) and compare them with similar features extracted from the model image.
Generally, object recognition systems have greater difficulty recognizing an object in a target image the greater the object's appearance differs from the appearance of the object in the model image. When the same object differs in appearance relative to the object in the model image, the object in the target image may be referred to as a “variant form” of the object in the model image.
FIG. 1 illustrates seven variants of the same object located in seven different images 110A-110G as just one example. In the images 110A-110G, the objects 120A-120G have the same general shape. In this example, the shape is the letter L located in the center of a square frame. In the image 110A, the object 120A is a solid object (i.e., of a generally uniform color) where the object 120A is in a color lighter than the background. In the image 110B, the object 120B is in a color darker than the background. Also, the image 110A and the image 110B have different polarities relative to one another. In the image 110C, the object 120C is a solid object with an accentuated outline 140C. In the image 110D, the object 120D includes a collection of solid objects 150D. In the image 110E, the object 120E is thicker than the object 120A of the image 110A (with the outline of the object 120A shown in phantom). In the image 110F, the object 120F is thinner than the object 120A (again with the outline of the object 120A shown in phantom). Note that it is the thickness and not the scale of the object that has changed in images 110E and 110F relative to image 110A. In the image 110G, the object 120G includes a collection of objects 150G, with an accentuated outline 140G. In addition, the polarity of the object 120G is reversed relative to the object 120A of the image 110A. Thus, the objects 120A-120G provide seven variant forms of an object that includes an L located in the center of a square frame.
Approaches have been proposed to allow object recognition systems to recognize variant forms of objects. However, these approaches have shortcomings because they are unable to directly recognize an object that appears in a variant form in a target image. Instead, a user must provide a model image for each of the expected forms. The current approaches are then constrained to work only with those expected forms. Further, generation of model images for all the various forms of an object is time consuming for the user burdened with generating the models. In some instances, models for all of the variant forms may not even be available. For example, machine vision systems are employed in semiconductor wafer manufacturing where variant forms of objects are naturally occurring and unpredictable.